Unveiling and Simulating Short-Video Addiction Behaviors via Economic Addiction Theory

TL;DR

This paper analyzes short-video addiction using economic addiction theory, leveraging large-scale behavioral data to model and simulate addictive behaviors, and proposes a novel framework, AddictSim, to mitigate addiction through personalized and diversity-aware strategies.

Contribution

It introduces a new approach combining economic addiction theory with large-scale behavioral data and develops AddictSim, a simulation framework that outperforms existing methods in modeling and mitigating short-video addiction.

Findings

AddictSim effectively models addictive behaviors in short-video platforms.

Diversity-aware algorithms within AddictSim reduce simulated addictive behaviors.

The approach aligns with traditional addiction patterns and social science findings.

Abstract

Short-video applications have attracted substantial user traffic. However, these platforms also foster problematic usage patterns, commonly referred to as short-video addiction, which pose risks to both user health and the sustainable development of platforms. Prior studies on this issue have primarily relied on questionnaires or volunteer-based data collection, which are often limited by small sample sizes and population biases. In contrast, short-video platforms have large-scale behavioral data, offering a valuable foundation for analyzing addictive behaviors. To examine addiction-aware behavior patterns, we combine economic addiction theory with users' implicit behavior captured by recommendation systems. Our analysis shows that short-video addiction follows functional patterns similar to traditional forms of addictive behavior (e.g., substance abuse) and that its intensity is consistent with findings from previous social science studies. To develop a simulator that can learn and model these patterns, we introduce a novel training framework, AddictSim. To consider the personalized addiction patterns, AddictSim uses a mean-to-adapted strategy with group relative policy optimization training. Experiments on two large-scale datasets show that AddictSim consistently outperforms existing training strategies. Our simulation results show that integrating diversity-aware algorithms can mitigate addictive behaviors well.